Journal of Advanced Research

Pathological activity within frontal cortical circuits is common in many neuropsychiatric disorders, such as obsessive-compulsive disorder (OCD). We developed an invasive brain mapping protocol in which temporary electrodes are implanted in candidate sites to identify personalized stimulation targets that can acutely relieve OCD symptoms. We found that stimulation within segments of the anterior limb of the internal capsule (ALIC) focally suppressed the structurally and functionally connected region of prefrontal and cingulate cortex. By leveraging the topographic organization of the ALIC, we reversibly inactivated frontal cortical sites with ALIC stimulation to determine which cortical regions are necessary for sustaining OCD symptoms. Stimulation of ventral capsule (VC) near the globus pallidus within the ALIC was associated with suppression of lateral orbitofrontal cortex activity and acute and long-term improvements in OCD symptoms. These results provide a paradigm for leveraging ALIC topography to deliver targeted connectomic neuromodulation to frontal cortex to treat neuropsychiatric disorders.

Background: Quantitative lipid assessment is central to identifying rupture-prone coronary plaques and represents a therapeutic target for lipid-lowering therapy. Near-infrared spectroscopy (NIRS)-derived lipid core burden index (LCBI) is well validated and widely used for detecting lipid-rich lesions. Optical frequency domain imaging (OFDI) is increasingly adopted for guiding percutaneous coronary intervention (PCI) due to its high-resolution structural imaging capabilities. Depolarization-sensitive OFDI (depOFDI) provides intrinsic lipid contrast and may enable combined structural and compositional plaque characterization within a single OFDI-based platform. Objective: To define an OFDI-derived lipid metric and evaluate its agreement with NIRS-derived LCBI. Methods: Thirty-three patients underwent both polarization-sensitive OFDI and NIRS-intravascular ultrasound imaging during PCI. After exclusion of 4 datasets, 29 co-registered pullbacks were analyzed. A signal-to-noise-corrected depolarization metric was used to identify lipid-rich regions and generate depOFDI chemograms. maxLCBI4mm value and location, as well as total LCBI, were computed and compared with NIRS. Results: depOFDI demonstrated strong agreement with NIRS, showing high correlation for maxLCBI4mm (r^2 = 0.862) and total LCBI (r^2 = 0.867), along with strong spatial concordance for the location of the maxLCBI4mm (r^2 = 0.900). Bland-Altman analysis of LCBI4mm showed minimal bias (10.7) with 95% limits of agreement of [81.4 to 102.8]. Conclusions: depOFDI enables accurate quantification of lipid burden alongside the high-resolution structural information inherently provided by OFDI. Because depolarization metrics can be derived from polarization-diverse detection available in many commercial OFDI systems, this approach provides a practical pathway toward comprehensive plaque characterization within existing PCI workflows, without the need for additional imaging modalities.

Clonal hematopoiesis of indeterminate potential (CHIP) represents the age-related expansion of hematopoietic stem cells with preleukemic mutations. However, its association with all-cause and cause-specific mortality has not been well characterized in older adults. We aimed to evaluate whether CHIP is associated with all-cause and cause-specific mortality in a population of older women in the United States. Our study included 6,704 participants in the Women?s Health Initiative Long Life Study (WHI-LLS) without hematologic malignancy. The co-primary exposures were any CHIP (variant allele frequency [VAF] [&ge;] 2%) and large CHIP (VAF [&ge;] 10%), and the primary outcome was all-cause mortality. Multivariable-adjusted Cox proportional hazards models tested the associations of CHIP and CHIP subtypes with all-cause and cause-specific mortality. Any CHIP and large CHIP were independently associated with all-cause mortality, with multivariable-adjusted hazard ratios (aHRs) of 1.12 (95% confidence interval [CI] 1.04-1.21; P = 0.003) and 1.28 (95% CI 1.15-1.43; P < 0.001), respectively. In gene-specific analyses, non-DNMT3A CHIP was associated with all-cause mortality (aHR: 1.22 [95% CI: 1.12-1.34], P < 0.001), while DNMT3A CHIP was not (aHR: 1.07 [95% CI: 0.98-1.18], P = 0.13). Furthermore, large CHIP was associated with cardiovascular (aHR: 1.29 [95% CI: 1.08-1.55], P = 0.006), cancer (aHR: 1.49 [95% CI: 1.11-2.02], P = 0.009), and neurologic (aHR: 1.40 [95% CI: 1.07-1.84], P = 0.02) death. In this cohort of older women, CHIP, particularly large clones and non-DNMT3A CHIP, was associated with all-cause and cause-specific mortality. These findings suggest that clonal size and subtype may differentially influence mortality risk.

Introduction: Incomplete recovery from relapses contributes to long-term disability accumulation in relapsing remitting multiple sclerosis (RRMS), yet the relationship between immune regulation and relapse recovery remains poorly defined. Objective: To longitudinally characterize regulatory/effector immune cell dynamics in untreated RRMS patients and assess their association with immune balance and relapse recovery. Methods: Monocytic myeloid-derived suppressor cells (M MDSCs), regulatory T cells (Treg), and effector CD4 T cell subsets were measured in blood from 69 untreated RRMS patients sampled during relapse or remission and reevaluated after 12 months. Associations with clinical recovery after relapse were examined. Results: During relapse, patients exhibited higher M MDSC and Treg frequencies than in remission, while effector T cell subsets remained unchanged. Over one year, M-MDSCs increased consistently regardless of baseline clinical status, whereas Treg frequencies remained stable. Effector to M MDSC ratios were markedly elevated during relapse and declined over time, while effector-to-Treg ratios showed minimal variation. M MDSC levels during relapse were associated with sustained regulatory features at 12 month follow up. Importantly, higher baseline M MDSC levels, but not Treg frequencies, were associated with complete relapse recovery at one year. Conclusion: These findings suggest that circulating M-MDSCs, but not Treg, reflect interindividual differences in immune regulation and clinical recovery after relapse in early RRMS.

The course of multiple sclerosis (MS) is highly heterogeneous, yet the biological mechanisms underlying this variability remain incompletely understood. Although metabolic alterations have increasingly been associated with disease progression, existing observational evidence is limited by confounding, reverse causation, and an inability to establish causal mechanisms. To bridge this gap, we used a metabolome-wide Mendelian Randomization (MR) framework, including thorough sensitivity analyses, to identify metabolites genetically linked to MS severity that can causally affect it. Bidirectional MR analyses revealed a subset of amino acid and lipid pathways with strong, consistent effects across different MR approaches, confirmed by tests for heterogeneity, horizontal pleiotropy, and LD confounding. For metabolites prioritized by metabolome-wide MR with evidence of causal effects, we conducted genetic colocalization at loci encompassing proximal enzyme-encoding genes, leveraging the corresponding instrumental variants to assess shared underlying genetic signals. This process revealed shared genetic signals between metabolite levels and MS severity, mapped to the FADS1/2 and CYP4F2 loci. A subsequent pathway-resolved set of cis-MR analyses across FADS1/2-derived polyunsaturated fatty acid (PUFA) metabolites, using a functional variant that proxies reduced {triangleup}5-desaturase activity, showed consistent effects indicating that FADS1 perturbation is associated with MS severity. Collectively, these results highlight FADS1 as a key driver of PUFA-related causal effects on MS severity in both systemic (circulating metabolites) and brain cell-specific contexts. Additional supportive cis-MR evidence implicates the disruption of CYP4F2 as another PUFA-metabolizing enzyme.

Background: Progressive multiple sclerosis (MS) is characterized by ongoing neurodegeneration and limited therapeutic options. Circulating metabolites provide insight into disease biology, yet biomarkers that predict disability progression and reflect treatment response are lacking. We aimed to identify metabolomic signatures associated with longitudinal MRI measures of brain atrophy and to evaluate whether ibudilast treatment was associated with metabolite trajectories over time. Methods: We repeatedly profiled 1,726 plasma metabolites using untargeted UPLC-MS/MS in 244 participants from the 96-week SPRINT-MS randomized trial of oral ibudilast, up to 100 mg daily, versus placebo. Weighted gene co-expression network analysis was used to derive groups of related metabolites. Associations between baseline metabolite groups and longitudinal MRI outcomes were evaluated using linear mixed-effects models adjusted for demographic, clinical, and treatment covariates. The primary outcome was the rate of whole-brain atrophy measured by brain parenchymal fraction (BPF), defined as the proportion of intracranial volume occupied by brain tissue. Secondary outcomes included white matter fraction (WMF), gray matter fraction (GMF), and cortical thickness (CTH). Metabolite groups nominally associated with MRI outcomes, defined as p < 0.05, were followed by individual metabolite analyses to identify potential drivers. Significant metabolites were tested for replication in a comparable real-world observational HEAL-MS cohort with longitudinal MRI data. Lastly, we tested whether ibudilast treatment was associated with metabolite trajectories and performed metabolite set enrichment analysis. Findings: Higher baseline levels of glycerophospholipids were associated with slower decline in both BPF and WMF, and sphingomyelins were similarly associated with slower BPF decline. For example, higher 1-palmityl-2-stearoyl-GPC (O-16:0/18:0) levels were associated with slower BPF decline in SPRINT-MS (beta = 0.016 [95% CI: 0.008, 0.024]; p = 4.35 x 10^-5) and replicated in HEAL-MS (beta = 0.108 [95% CI: 0.006, 0.211]; p = 3.90 x 10^-2). Metabolites associated with GMF preservation were enriched in androgenic steroids and steroid sulfates, with consistent positive associations observed in the replication cohort, whereas metabolites inversely associated with CTH were predominantly xenobiotic-related. Ibudilast treatment was associated with increased sphingomyelin species, such as palmitoyl sphingomyelin (d18:1/16:0; beta = 0.185 [95% CI: 0.085, 0.286]; FDR = 1.79 x 10^-2), and decreased levels of amino acid-related metabolites, such as anthranilate (beta = -0.270 [95% CI: -0.403, -0.137]; FDR = 3.87 x 10^-2). Pathway-based analyses corroborated these findings, highlighting glycerophospholipid and sphingolipid metabolism as key pathways implicated in brain atrophy in MS. Interpretation: Distinct lipid subsets were associated with slower brain atrophy in people with MS, and ibudilast treatment was associated with metabolite alterations in potentially neuroprotective directions. Metabolomics may provide prognostic and pharmacodynamic biomarkers for progressive MS.

Background: Pediatric dilated cardiomyopathy (DCM) is a leading cause of heart failure and transplantation, with variable prognosis and high early mortality. This study developed and validated a nomogram predicting short-term mortality risk to guide clinical decisions. Methods: The data were sourced from the Pediatric Cardiomyopathy Database at Shandong Provincial Hospital. Cox regression analysis was conducted to determine outcome-associated factors, and a nomogram was developed to estimate 1, 3, and 5year mortality risks for children with DCM. Model effectiveness was assessed through the concordance index (C-index) and area under the receiver operating characteristic curve (AUC). Additionally, calibration curves and decision curve analysis (DCA) were employed to evaluate the model's predictive accuracy and clinical relevance. Results: A cohort of 106 children diagnosed with primary DCM and who underwent genetic analysis was studied, with a median diagnostic age of 10 months (ranging from 5 to 84 months), comprising 50 girls (47.2%). The rate of detecting genetic mutations was 28.3%, uncovering 14 gene variants linked to DCM, with TTN mutations being the most common. Both univariate and multivariate Cox regression analyses indicated that both sex and NT-proBNP levels had a significant impact on survival rates among pediatric DCM patients.The model exhibited strong discriminative performance, calibration, and clinical net benefit, as assessed by the C-index, calibration plots, and decision curve analysis (DCA). Conclusions: The prediction model created in this research shows strong accuracy in forecasting survival rates at 1, 3, and 5 years for children with DCM, highlighting its significant relevance in clinical settings.

Abstract Introduction The ARRIVE trial first demonstrated that elective induction of labour (IOL) at 39 weeks in low-risk pregnancies reduced the likelihood of caesarean section (CS) without compromising perinatal safety; however, the generalizability of these findings remains debated, leading to uncertainty in clinical practice. The LIRIC trial aims to evaluate whether 39-week elective IOL reduces CS rates compared with expectant management, while exploring its impact on infant neurodevelopment and multi-omics profiles. Methods and analysis This is a single-centre, open-label, randomized controlled trial in China. A total of 1,074 low-risk pregnant women (nulliparous or multiparous) will be randomly assigned (1:1 ratio) to either 39-week IOL or expectant management. The primary outcome is the caesarean section (CS) rate. Secondary outcomes include a composite of severe neonatal morbidity and perinatal mortality and infant neurodevelopmental scores (Bayley-4 and ASQ-3), among others. Data analysis will follow the Intention-to-Treat (ITT) principle. Biospecimen will be collected for metagenomic and metabolomic analyses, with results to be reported separately. Ethics and dissemination The protocol has been approved by the Ethics Committee of Women's Hospital, School of Medicine, Zhejiang University. Informed consent will be obtained from all participants. Results will be disseminated via peer-reviewed journals, and standardized infant developmental reports will be provided to participants to enhance study benefit. Trial registration number NCT07082530.

Diastolic heart failure (HF) in primary cardiomyopathy is under-recognized and often diagnosed late, particularly in children. While recent studies have advanced understanding of HF with preserved ejection fraction in older adults, the prevalence, outcomes and molecular drivers of diastolic HF in pediatric and young adult cardiomyopathy remain poorly defined, where disease is typically driven by primary myocardial disease rather than acquired co-morbidities. The Canadian Cardiomyopathy Collaborative (C3) was assembled to leverage three of Canadas leading pediatric and adult cardiomyopathy biobank registries. Its flagship initiative, Artificial Intelligence to Model Diastolic Heart Failure (AID-HF), aims to integrate deep phenotyping - including comprehensive diastolic function assessment - with genomics, lipidomics and proteomics and apply machine learning to identify biological and clinical signatures that drive cardiac function and outcomes in cardiomyopathy. Harmonized phenotyping and multiomics protocols across registries will create a uniquely integrated national data resource and enable the goals of AID-HF i.e., earlier diagnosis and new therapeutic targets for diastolic HF in cardiomyopathy.

Background: Comorbidities are common in multiple sclerosis (MS) and may influence disability outcomes, but their dynamic impact on bidirectional disability transitions and long-term disability remains incompletely understood. Better understanding of this longitudinal relationship could inform personalized disability management strategies for people with MS. Methods: We leveraged two large electronic health record (EHR)-linked MS registries and applied multi-state Markov models (MSMs) to examine the extent to which individual comorbidities and overall comorbidity burden were associated with short-term disability transitions, long-term disability transition probabilities, and expected time spent in each disability state. We additionally compared MSM-based predictions of confirmed disability worsening (CDW) with Cox proportional hazards (CoxPH) model-based predictions using the integrated Brier score with bootstrap validation. Results: Among 3,723 patients with MS (74.6% female; 86.2% non-Hispanic White; mean age=41.9 years; mean disease duration=5.4 years) contributing 41,860 disability assessments over a mean follow-up of 7.3 years, higher cardiometabolic and psychiatric comorbidity burden was associated with increased transition intensity toward worse disability states and decreased transition intensity toward improvement, with a stepwise gradient across burden levels. Compared with patients without comorbidities, those with [&ge;]4 comorbidities had a 28% higher risk of worsening (HR=1.28 [1.06, 1.55]) and a 20% lower risk of improvement (HR=0.80 [0.67, 0.95]). Each individual comorbidity was significantly associated with worse disability transitions. Long-term estimates indicated a higher 5-year probability of severe disability and fewer years spent in the no-disability state among patients with greater comorbidity burden. CoxPH models showed directionally consistent associations but lower predictive accuracy for CDW compared with MSMs. Conclusion: Cardiometabolic and psychiatric comorbidities are associated with worse disability trajectories in MS, reducing improvement and accelerating progression. By providing a nuanced framework to quantify short-term disability transitions and long-term disability patterns, MSMs may have real-world clinical utility in disability prediction.

ABSTRACT OBJECTIVE: We performed a systematic review and meta-analysis (SRMA) of post-surgical outcomes, comparing chlorhexidine gluconate (CHG) versus povidone iodine (PI) for vaginal antisepsis of major gynecologic procedures. DATA SOURCES: Ovid Medline, Embase, Scopus, Embase, Cochrane, and Clinicaltrials.gov were searched between 1986 and December 2023, for studies comparing CHG with PI for vaginal antisepsis of major gynecologic operations. STUDY ELIGIBILITY CRITERIA: We included Randomized Controlled Trials (RCTs) and non-RCTs comparing CHG to PI for vaginal antisepsis of major gynecologic operations. The primary outcome was surgical site infections (SSIs) and the secondary outcome was urinary tract infections (UTIs) and vaginal irritation. METHODS: Summary estimates were calculated by fixed effects models when I2 [&le;] 25% and by random effects models when I2 > 25%. Statistical analysis was performed using RevMan 5.4.1. The protocol for this systematic review was registered on PROSPERO (ID CRD42022378101). RESULTS: Nine studies met the inclusion criteria, four of which were randomized controlled trials (RCTs). 9538 patients were included, 4300 (45%) of whom were allocated to CHG and 5238 (55%) to PI. No statistically significant difference in SSI incidence was found for vaginal antisepsis with CHG versus PI in pooled analyses (n= 9538 patients; RR 1.20; 95% CI 0.92-1.57; I2 =0%). In contrast, a significantly higher risk of UTIs was observed for vaginal antisepsis with CHG than with PI (n=6061 patients; RR 1.48 95% CI 1.03-2.14; I2 = 0%). CONCLUSION: In our SRMA, there were no significant differences in SSI risk when either CHG or PI was utilized for antiseptic vaginal preparation. Interestingly, vaginal antisepsis with PI was associated with a lower incidence of post-operative UTIs following major gynecologic surgery. Our findings support current guidelines that form of vaginal antisepsis can be used for SSI prevention. They also suggest that PI may result in fewer postoperative UTIs but further randomized studies are needed to support these findings. Key words: surgical site infection, surgical wound infection, urinary tract infection, urogynecologic surgery, Chlorhexidine, Povidone Iodine, surgical antiseptic,

INTRODUCTION: Alzheimers disease (AD) is a multifactorial disorder, yet current research largely focuses on downstream biomarkers with limited attention to environmental contributors. Experimental studies suggest that per and polyfluoroalkyl substances (PFAS) may contribute to neuroimmune and neurodegenerative pathways relevant to AD. OBJECTIVE: To examine associations between PFAS exposure and neuroimmune and AD related plasma biomarkers in cognitively unimpaired rural adults. METHODS: In a cross sectional pilot study (n=48), serum concentrations of 33 PFAS were measured, including four legacy compounds (PFOS, PFHxS, PFOA, PFNA). Plasma neuroimmune related (ITGB2, SMOC1, TREM2, GFAP) and AD related biomarkers (Ab42/40, ptau217) were detected using proteomic analysis. RESULTS: PFOS showed moderate associations with ITGB2, SMOC1, and Ab42/40 in unadjusted analyses, which attenuated after adjustment for age. PFOA and PFNA demonstrated consistent inverse associations with TREM2 before and after adjustment. DISCUSSION: Findings suggest possible compound specific PFAS associations with immune and amyloid related biomarkers, supporting further investigation in longitudinal and PFAS mixture based studies.

Background: Store-and-forward teledermatology commonly relies on several patient-submitted photographs of the same concern, but most dermatology artificial intelligence models classify single images independently. Objective: To develop and internally validate a case-level diagnostic-support model that aggregates multiple patient-submitted photographs for common dermatologic conditions. Methods: We conducted a retrospective diagnostic-modeling study using the Skin Condition Image Network, a public dataset of deidentified self-taken dermatology images from US adults. We curated 2,336 cases comprising 5,041 images across 10 common inflammatory, allergic, and infectious conditions. Cases were split at the submission level into training, validation, and held-out test sets. Frozen general-purpose and dermatology-specific encoders were compared with image-level classifiers and a gated-attention multiple instance learning model that generated one case-level output from 1-3 images. Results: The strongest image-level baseline, dermatology-specific embeddings with random forest classification, achieved macro/micro ROC-AUCs of 0.797/0.854. Case-level aggregation improved discrimination, with dermatology-specific embeddings plus multiple instance learning achieving mean macro/micro ROC-AUCs of 0.819/0.863 across repeated stratified experiments. The locked final model achieved macro/micro ROC-AUCs of 0.800/0.849 on the held-out test set. Balanced-threshold sensitivity/specificity examples were 0.702/0.688 for eczema and 0.818/0.826 for urticaria. Limitations: Internal validation used a 10-condition subset from a US volunteer dataset; external validation, calibration, subgroup performance analysis, and prospective workflow studies are required. Conclusion: Modeling the teledermatology submission as a multi-image case better reflects asynchronous dermatology workflow than single-image classification. The model is preliminary clinician-facing support for structured review and triage, not autonomous diagnosis.

Tuberculosis (TB) remains one of the deadliest infectious diseases, with over a million deaths annually and a growing threat from multidrug-resistant strains (MDR-TB). A major bottleneck in controlling TB is the lack of truly portable, rapid, and user-friendly diagnostic systems that can operate effectively in decentralized, resource-constrained settings. Here, we present a first-of-its-kind, portable nucleic-acid-based diagnostic platform that enables both primary TB screening and detection of drug resistance within the same unified framework, without any change in the operative embodiment. The system integrates loop-mediated isothermal amplification (LAMP) targeting dual Mycobacterium tuberculosis markers (IS6110 and IS1081) with a compact, AI-enabled device and smartphone-based readout, delivering rapid and reliable results at the point-of-care. Clinical evaluation across 105 samples demonstrated high sensitivity and specificity. Further validation through real-world deployment in a primary healthcare setting, using a single-gene (IS6110) configuration operated by minimally trained personnel, yielded 95.60% sensitivity and 100% specificity, benchmarked against GeneXpert. Critically, the same platform architecture, without modification, extends seamlessly to drug-resistance profiling, demonstrated here through a probe-free, allele-specific LAMP approach for identifying key mutations associated with rifampicin (rpoB) and isoniazid (katG) resistance. By combining robust molecular diagnostics with AI-driven automation in a compact and accessible format, this work represents a significant medical advancement toward democratizing TB care. The platform thus holds strong potential to enable early screening, guide timely treatment decisions, reduce transmission, and substantially strengthen global TB elimination efforts, particularly in high-burden, low-resource settings.

Objective Astrocyte activation is increasingly recognized as an important component of multiple sclerosis (MS) pathology. Natalizumab (NTZ), a highly effective therapy for relapsing-remitting MS (RRMS), primarily blocks leukocyte trafficking into the central nervous system. However, its effects on astrocytic metabolism remain unclear. We investigated astrocyte-associated metabolic changes after NTZ treatment using quantitative 1-11C-acetate positron emission tomography (PET). Methods Seven patients with RRMS underwent quantitative 1-11C-acetate PET before and after NTZ treatment. PET-derived k2, an index of oxidative acetate metabolism, was analyzed voxel-wise and within GM and white-matter volumes of interest. Clinical status and brain magnetic resonance imaging (MRI) findings were assessed, and cognitive performance was evaluated using Rao's Brief Repeatable Battery of Neuropsychological Tests. Results After NTZ treatment, k2 decreased in all patients compared with pretreatment levels. Both gray and white matter showed significant reductions, and voxel-based analysis demonstrated widespread decreases across cortical and subcortical regions of the cerebrum and cerebellum, with no regions showing significant posttreatment increases. MRI showed no worsening; Expanded Disability Status Scale scores were stable or improved, and cognitive performance was generally stable, with improvements in selected subtests. Interpretation Quantitative 1-11C-acetate PET demonstrated a whole-brain reduction in astrocyte-associated metabolism after NTZ treatment in RRMS, most prominently in gray matter. NTZ may modulate astrocyte activity, in addition to its established effects on peripheral immune cell trafficking.

Purpose: To characterize peripheral immune alterations in treated birdshot uveitis (BU) patients using high-dimensional mass cytometry and multiplex serology. Design: Cohort study. Subjects: 36 BU patients on immunomodulatory treatment (IMT) and 31 healthy controls (HCs). Methods: Detailed ophthalmologic examinations were performed, and peripheral blood and serum samples were collected for immune profiling using mass cytometry and multiplex cytokine analysis. Main Outcome Measures: Imaging-based indicators of ocular inflammation; peripheral immune cell frequencies; serum cytokine levels. Results: Compared to HCs, BU patients showed increased frequencies of Th17, CD146+ T cells, intermediate effector/central memory T cells co-expressing CXCR3 and CCR4, CD56dim NK cells and elevated IL-18 levels. Patients were clinically stratified by an expert ophthalmologist into three disease activity groups: Inactive, Active (comprising combinations of surface retina, deep retina and choroid activity) and Burned-out. Inactive patients harbored more quiescent effector T cells, e.g. Tim-3+ Tc17-Tc22 intermediates and more CD8+ TSCM, potentially representing a resting pool of autoimmune T cells. Active patients exhibited increased in vivo activation of relevant T cells, with stronger HLA-DR, CD38 or PD-1 expression, and highest levels of CD56dim NK cells. Immune profiles were also linked to treatment subgroups: csDMARDs (conventional synthetic disease-modifying antirheumatic drugs) were associated with higher CD56bright NK frequencies, and absence of therapy showed elevated PD-1/SLAMF7 Tc17+1 and PD-1CD57 CD8 TEMRA cells. IL-6R blockade (tocilizumab) resulted in loss of IL-6R T-cells accompanied by increased SLAMF7 T cells, due to epitope masking. Conclusions: Peripheral CyTOF profiling anchored to thorough clinical stratification revealed disease activity-associated immune signatures and therapy-associated imprints in BU.

Background and Aims: Clinical interpretation of the precordial leads V1-V6 assumes that Wilson's central terminal (WCT) has a fixed anatomical location. Consequently, a positive signal corresponds to electrical activation spreading from WCT towards the respective electrode, and vice versa. However, the location of WCT has never been systematically investigated. Yet, a better understanding of WCT location could improve the interpretation of the precordial leads. This work aims to characterize the spatial expansion and location of the physical WCT i.e., the electrical potential defined by the WCT, during the P-wave on the body surface. Methods: An intensive analysis of body surface potential maps (BSPMs) during atrial depolarization in an in silico patient cohort and clinical data was conducted. Results: During the P-wave, the location of WCT was not stationary but the spatial extent and location varied across time as well as across individuals. Four distinct spatial patterns of WCT distribution on the body surface were identified in silico, and three of these were found in the clinical cohort. WCT signals agreed with BSPM signals at commonly assumed positions of WCT only for a small fraction of the P-wave. Conclusion: The spatial extension and location of WCT changes during the P-wave and thus should be considered when interpreting the precordial leads.

Blood biomarker models are increasingly used in Alzheimer's disease and related dementia translational research, but predictive performance can be inflated when the same dataset is used for both model development and evaluation. We assess the effect of data double dipping using simulations and NULISA proteomic data from the MYHAT-NI community-based cohort to predict brain amyloid-beta neuroimaging status. In both settings, training AUC increased as more biomarkers were added, while testing AUC peaked earlier and then declined. These findings show that data double dipping can inflate model performance and highlight the need for external validation or internal validation with data partitioning.

Background: Citrin deficiency, caused by biallelic pathogenic variants in SLC25A13, must be identified early to prevent serious complications such as hyperammonemia and liver failure. However, clinical diagnosis is often delayed due to its nonspecific presentation and limited sensitivity of amino acid-based newborn screening methods. Although genome-based evaluations are being investigated to address these issues, concerns about their cost, turnaround time, variant interpretation ability, and data handling highlight the need for a more practical yet reliable alternative. We investigated the feasibility of applying proteomic approach on dried blood spots (DBS), which are routinely used in newborn screening. Methods: We performed untargeted liquid chromatography-tandem mass spectrometry to analyze the proteome of DBS using a previously developed "non-targeted analysis of non-specifically DBS-absorbed proteins" (NANDA) workflow. SLC25A13 protein abundance was quantified in individuals with biallelic loss-of-function mutations, compound loss-of-function/missense mutations, and heterozygous carriers; this was also evaluated in healthy and diseased controls representing relevant differential diagnoses. To leverage proteomic information, we derived a multivariate proteomic signature using feature selection and evaluated its performance with leave-one-out cross-validation. Biological relevance was assessed by enrichment analysis, and complementary transcriptomics was performed using RNA sequencing. Results: A total of 7,474 proteins, including SLC25A13, were consistently detected in DBS. SLC25A13 was undetectable in individuals with biallelic loss-of-function mutations. However, individuals with compound loss-of-function/missense genotypes showed reduced but measurable SLC25A13 levels, comparable to those observed in heterozygous carriers. In contrast, a compact 15-protein signature accurately identified individuals with compound loss-of-function/missense genotypes (AUC, 0.99; sensitivity, 1.00; specificity, 0.95). The signature was enriched for Ca2+-response, and transcriptomics showed downregulation of genes related to multimodal ion channels in affected individuals compared to controls. Conclusions: DBS-based proteomic profiling may assist in the diagnosis of citrin deficiency through SLC25A13-quantification and a biologically plausible multivariate signature. More broadly, this strategy offers a promising new diagnostic layer for protein disorders, providing a proteomic readout in a clinically practical DBS format with potential utility for future diagnostic and screening applications.

Background: Regular exercise is a highly effective yet underutilized strategy to reduce cardiometabolic disease burden. Whether brief structured exercise programs confer lasting cardiometabolic benefits remains unclear. The STRRIDE-Prediabetes Reunion study examined legacy effects of exercise training on cardiorespiratory fitness, body composition, and cardiometabolic health. Methods: Seventy-three participants (71.3 {+/-} 7.2 years; 64% women; 77% White) completed Reunion assessments ~11 years after completing one of four 6-month interventions differing in exercise amount, intensity, and inclusion of diet-induced weight loss. Linear mixed effects models evaluated longitudinal trajectories; secondary analyses examined baseline-adjusted associations among short-term intervention response and Reunion outcomes. Results: Abdominal adiposity improved across all groups from baseline to Reunion, with waist circumference decreasing ~3 cm over the follow-up period. In contrast, cardiorespiratory fitness and fat-free mass declined significantly. A significant group by time interaction was observed for total fat mass (p=0.01), with continued fat mass reductions observed in women randomized to high amount exercise. After baseline adjustment, greater short-term intervention response was associated with more favorable Reunion outcomes across fitness, body composition, and cardiometabolic domains; fat-free mass showed the strongest association ({beta}=0.84, p<0.0001). Conclusions: In older adults with prediabetes, the STRRIDE-Prediabetes interventions produced several legacy health effects persisting more than a decade later. Legacy effects differed by sex and exercise dose, and short-term intervention response relative to baseline was associated with long-term outcomes, supporting targeted exercise strategies to preserve cardiometabolic health and functional independence with aging.